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Geometric Image Labeling with Global Convex Labeling Constraints

  • Artjom ZernEmail author
  • Karl Rohr
  • Christoph Schnörr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10746)

Abstract

In [2], a smooth geometric labeling approach was introduced by following the Riemannian gradient flow of a given objective function on the so-called assignment manifold. The approach evaluates a user-defined data term and performs spatial regularization by Riemannian averaging of the assignment vectors. In this paper, we extend this approach in order to impose global convex constraints on the labeling results based on linear filter statistics in the label space. The smoothness of the approach is preserved by using logarithmic barrier functions to handle the new constraints. We discuss how suitable filters can be determined from example data of a given image class, and we demonstrate numerically the effectiveness of the constraints in several academic labeling scenarios.

Keywords

Image labeling Assignment manifold Statistical label constraints Riemannian gradient flow Information geometry 

Notes

Acknowledgments

We gratefully acknowledge support by the German Science Foundation, grant GRK 1653.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Image and Pattern Analysis GroupHeidelberg UniversityHeidelbergGermany
  2. 2.Biomedical Computer Vision Group, BIOQUANTHeidelberg UniversityHeidelbergGermany

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